Event Causality Extraction (ECE) aims at extracting causal event pairs from texts. Despite ChatGPT's recent success, fine-tuning small models remains the best approach for the ECE task. However, existing fine-tuning based ECE methods cannot address all three key challenges in ECE simultaneously: 1) Complex Causality Extraction, where multiple causal-effect pairs occur within a single sentence; 2) Subtask~ Interaction, which involves modeling the mutual dependence between the two subtasks of ECE, i.e., extracting events and identifying the causal relationship between extracted events; and 3) Knowledge Fusion, which requires effectively fusing the knowledge in two modalities, i.e., the expressive pretrained language models and the structured knowledge graphs. In this paper, we propose a unified ECE framework (UniCE to address all three issues in ECE simultaneously. Specifically, we design a subtask interaction mechanism to enable mutual interaction between the two ECE subtasks. Besides, we design a knowledge fusion mechanism to fuse knowledge in the two modalities. Furthermore, we employ separate decoders for each subtask to facilitate complex causality extraction. Experiments on three benchmark datasets demonstrate that our method achieves state-of-the-art performance and outperforms ChatGPT with a margin of at least 30% F1-score. More importantly, our model can also be used to effectively improve the ECE performance of ChatGPT via in-context learning.
Event Causality Identification with Causal News Corpus -- Shared Task 3, CASE 2022
University of Groningen（格罗宁根大学）；Mersin University（梅尔辛大学）；University of East Anglia（东英吉利大学）；Radboud University（拉德堡德大学）
CASE 2022的事件因果关系识别共享任务涉及两个子任务，它们在因果新闻语料库上工作。子任务1要求参与者预测一个句子是否包含因果关系。这是一个有监督的二元分类任务。子任务2要求参与者识别每个因果句的原因、结果和信号跨度。这可以被看作是一个有监督的序列标记任务。对于这两个子任务，参与者上传了他们对一个测试集的预测，并分别根据子任务1和2的二进制F1和宏F1分数进行排名。本文总结了17个团队的工作，提交了他们的结果，我们的竞争和12个系统描述文件，收到。子任务1和2的最佳F1得分分别为86.19%和54.15%。所有表现最好的方法都涉及预先训练的语言模型，这些模型针对目标任务进行了微调。我们在本文中进一步讨论了这些方法，并分析了参与者系统中的错误。
Enhancing LLM Character-Level Manipulation via Divide and Conquer
University of Southern California（南加州大学）；The University of Queensland（昆士兰大学）；National University of Singapore（新加坡国立大学）；University of California, Los Angeles（加州大学洛杉矶分校）；University of California, San Diego（加州大学圣迭戈分校）；University of California, Merced（加州大学默塞德分校）
Enhancing Text-to-SQL Capabilities of Large Language Models via Domain Database Knowledge Injection
School of Cyber Science Engineering, Huazhong University of Science and Technology（华中科技大学网络空间科学与工程学院）；Wuhan AI Research Foundation Model Research Center（武汉人工智能研究基金会大模型研究中心）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Peng Cheng Laboratory（鹏城实验室）
TUMS: Enhancing Tool-use Abilities of LLMs with Multi-structure Handlers
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Nanjing Artificial Intelligence Research of IA（南京人工智能研究所）；University of Chinese Academy of Sciences（中国科学院大学）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Nanjing University of Information Science & Technology（南京信息职业技术学院）
IIE-NLP-Eyas at SemEval-2021 Task 4: Enhancing PLM for ReCAM with Special Tokens, Re-Ranking, Siamese Encoders and Back Translation
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）
Enhancing Depression-Diagnosis-Oriented Chat with Psychological State Tracking
School of Computer Science and Technology, East China Normal University（东华大学计算机科学与技术学院）；Shanghai Institute of Al for Education, East China Normal University（东华大学上海人工智能教育研究所）；School of Psychology and Cognitive Science, East China Normal University（东华大学心理学与认知科学学院）
RoadMapper: A Multi-Agent System for Roadmap Generation of Solving Complex Research Problems
Beijing University of Posts and Telecommunications（北京邮电大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；IDEA Research（IDEA研究院）；Hithink RoyalFlush Information Network Co., Ltd.（Hithink RoyalFlush信息网络有限公司）
